a new bit-wise approach for image-in-audio steganography using deep learning

This work proposes a novel steganographic scheme that employs deep learning to embed rgb images into audio files, and it also introduces an innovative steganalysis approach. the proposed method embeds an image at the bit level within the audio in the frequency domain, enhancing flexibility for embedding various data types. the network uses an encoder-decoder architecture, where the encoder embeds bits into the audio, and the decoder extracts the embedded bits from the audio. to enhance the information transmission rate, an image compression method based on the yuv color model is used. this method can reduce the data to be hidden and transmitted for the image by up to 50%. the steganographic encoder-decoder architecture incorporates multiple paths to facilitate gradient flow and network training. the proposed steganalysis network effectively detects stego audio files containing hidden messages by analyzing the signal’s transform domain features. the results demonstrate the proposed steganography scheme’s enhanced security, with audio signal-to-noise ratio (snr) ranging from 26.9 to 39.6 db and image peak signal-to-noise ratio (psnr) from 19.02 to 34.8 db. compared to other audio steganography schemes, the proposed method is shown to have a higher performance in terms of audio cover perceptibility and hidden image quality.